> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.nvidia.com/dynamo/llms.txt. For full content including API reference and SDK examples, see https://docs.nvidia.com/dynamo/llms-full.txt. # v1.2.1 ## Docs - [Quickstart](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/quickstart.md): Get a Dynamo OpenAI-compatible endpoint running in a container in about 5 minutes. - [Introduction to Dynamo](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/introduction.md) - [Local Installation](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/local-installation.md): Install and run Dynamo on a local machine or VM with containers or PyPI - [Building from Source](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/building-from-source.md): Build Dynamo from source for development and contributions - [Kubernetes Deployment](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/kubernetes-deployment.md): Use Dynamo's Kubernetes-native path when you are ready to deploy on a GPU cluster. - [Contribution Guide](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/contribution-guide.md): How to contribute to Dynamo - [Documentation Style Guide](https://docs.nvidia.com/dynamo/v1.2.1/getting-started/documentation-style-guide.md): How to write and structure Dynamo docs, examples, and recipes - [Support Matrix](https://docs.nvidia.com/dynamo/v1.2.1/resources/support-matrix.md): Hardware, software, and build compatibility for Dynamo - [Feature Matrix](https://docs.nvidia.com/dynamo/v1.2.1/resources/feature-matrix.md) - [Release Artifacts](https://docs.nvidia.com/dynamo/v1.2.1/resources/release-artifacts.md): Container images, Python wheels, Helm charts, Rust crates, and release history - [Examples](https://docs.nvidia.com/dynamo/v1.2.1/resources/examples.md) - [Glossary](https://docs.nvidia.com/dynamo/v1.2.1/resources/glossary.md) - [Dynamo Blog](https://docs.nvidia.com/dynamo/v1.2.1/digest.md): Engineering deep dives, architecture notes, and project updates from the NVIDIA Dynamo team. - [NVIDIA Dynamo Snapshot: Fast Startup for Inference Workloads on Kubernetes](https://docs.nvidia.com/dynamo/v1.2.1/digest/dynamo-snapshot-fast-startup.md): NVIDIA Dynamo Snapshot combines CUDA and host checkpointing to restore warm inference workers quickly on Kubernetes. - [DynoSim: Simulating the Pareto Frontier](https://docs.nvidia.com/dynamo/v1.2.1/digest/dynosim-pareto-frontier.md): DynoSim is a workload-driven discrete-event simulation of the NVIDIA Dynamo serving stack for mapping Pareto frontiers before real-cluster validation. - [Dynamo Day 0 support for TokenSpeed](https://docs.nvidia.com/dynamo/v1.2.1/digest/tokenspeed-day-0.md): Dynamo adds day-0 TokenSpeed support with the Dynamo frontend for Kimi K2.5. - [Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in Dynamo](https://docs.nvidia.com/dynamo/v1.2.1/digest/agentic-harnesses.md): Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in Dynamo - [Full-Stack Optimizations for Agentic Inference with Dynamo](https://docs.nvidia.com/dynamo/v1.2.1/digest/agentic-inference.md): How Dynamo optimizes for agentic workloads at three layers: frontend API, router, and KV cache management. - [Flash Indexer: A Story of Inter-Galactic KV Routing](https://docs.nvidia.com/dynamo/v1.2.1/digest/flash-indexer.md): Dynamo's Flash Indexer tracks every cached KV block across all inference workers at 170M ops/s. Six iterations of data structure design got it there. - [Kubernetes Quickstart](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/start-here/kubernetes-quickstart.md) - [Installation Guide](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/start-here/installation-guide.md) - [Dynamo Operator](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/start-here/dynamo-operator.md) - [Minikube Setup](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/start-here/minikube-setup.md) - [Deployment Overview](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/deploy-models/model-deployment-guide.md): Understand DynamoGraphDeployments, DynamoComponentDeployments, DGDR, and recipes - [Managing Models with DynamoModel](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/deploy-models/managing-models-with-dynamo-model.md) - [DGDR Reference](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/deploy-models/dgdr-reference.md) - [Model Caching](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/model-loading/model-caching.md): Download models once and share across all pods in a Kubernetes cluster - [ModelExpress](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/model-loading/model-express.md): Speed up model weight distribution across Kubernetes workers - [Autoscaling](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/autoscaling.md) - [Rolling Updates](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/rolling-update.md) - [Disaggregated Inference Communication Guide](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/disagg-communication.md): Best practices for prefill/decode worker communication on Kubernetes - [Topology-Aware KV Transfer](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/topology-aware-kv-transfer.md): Keep disaggregated prefill and decode KV-cache transfers within a selected topology domain - [Metrics](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/observability/metrics.md) - [Logging](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/observability/logging.md) - [Operator Metrics](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/operate/observability/operator-metrics.md) - [Multinode Deployments](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/scale/multinode-deployments.md) - [Grove](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/scale/grove.md) - [Topology Aware Scheduling](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/scale/topology-aware-scheduling.md) - [Service Discovery](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/advanced-platform/service-discovery.md) - [Webhooks](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/advanced-platform/webhooks.md) - [Snapshotting GPU Workers](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/advanced-platform/snapshot.md) - [Shadow Engine Failover](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/advanced-platform/shadow-engine-failover.md) - [Developing the Operator with Tilt](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/advanced-platform/developing-with-tilt.md): Fast, live-reload development loop for the Dynamo Kubernetes operator - [Amazon Elastic Kubernetes Service (EKS)](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/aws/eks-setup.md) - [EFA (RDMA over AWS Fabric) on EKS](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/aws/efa-rdma-over-aws-fabric.md) - [Amazon EFS Setup for EKS](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/aws/efs.md) - [Amazon Elastic Container Service (ECS)](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/aws/ecs.md) - [Azure Kubernetes Service (AKS)](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/azure/aks-setup.md) - [RDMA / InfiniBand on AKS](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/azure/rdma-infini-band.md) - [Storage for Model Caching on AKS](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/azure/aks-storage.md) - [Azure Lustre CSI Driver for AKS](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/azure/azure-lustre-csi-driver.md) - [AKS Spot VMs](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/azure/spot-v-ms.md) - [Google Kubernetes Engine (GKE)](https://docs.nvidia.com/dynamo/v1.2.1/kubernetes-deployment/cloud-provider-guides/gcp/gke-setup.md) - [Feature Guides](https://docs.nvidia.com/dynamo/v1.2.1/user-guides.md): Start with Dynamo's core serving optimizations, then branch into operations and model capabilities. - [Router Guide](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/kv-cache-aware-routing.md): Deployment modes, quick start, and page map for Dynamo routing docs - [Disaggregated Serving](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/disaggregated-serving.md): Split prefill and decode into independently scalable worker pools - [Sizing with AIConfigurator](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/disaggregated-serving/sizing-with-ai-configurator.md): Compare aggregated and disaggregated layouts before deployment - [KVBM Guide](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/kv-cache-offloading.md): Enable KV offloading using KV Block Manager (KVBM) for Dynamo deployments - [Dynamo Benchmarking](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/benchmarking.md): Benchmark and compare performance across Dynamo deployment configurations - [Tool Calling & Reasoning Parsing](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing.md): Parse tool calls and reasoning out of model output into OpenAI-compatible tool_calls and reasoning_content - [Tool Call Parsing (Dynamo)](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/tool-call-parsing-dynamo.md): Connect Dynamo to external tools and services using Dynamo's built-in tool call parsers - [Reasoning Parsing (Dynamo)](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/reasoning-parsing-dynamo.md): Configure Dynamo's built-in reasoning parsers for models that emit thinking content - [Parser Engine Fallback](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/parser-engine-fallback.md): Use upstream vLLM or SGLang tool-call and reasoning parsers when Dynamo does not ship one - [Parser Configuration](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/parser-configuration.md): How --dyn-chat-processor, --dyn-tool-call-parser, and --dyn-reasoning-parser fit together - [Tool Calling Probe Snapshot for Dynamo 1.2](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/tool-calling-probe-snapshot-for-dynamo-1-2.md): Static release snapshot of tool-calling probe results across supported model families - [Troubleshooting Tool Calls](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/parsing/troubleshooting-tool-calls.md): Capture raw model output with logprobs so issues can be localized - [Fault Tolerance](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance.md): Handle failures gracefully with request migration, cancellation, and graceful shutdown - [Request Migration](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance/request-migration.md) - [Request Cancellation](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance/request-cancellation.md) - [Request Rejection](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance/request-rejection.md) - [Graceful Shutdown](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance/graceful-shutdown.md) - [Testing](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fault-tolerance/testing.md) - [Observability (Local)](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local.md): Monitor Dynamo deployments with metrics, logging, and tracing - [Prometheus + Grafana Setup](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/prometheus-grafana-setup.md) - [Metrics](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/metrics.md) - [Metrics Developer Guide](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/metrics-developer-guide.md) - [Health Checks](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/health-checks.md) - [Tracing](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/tracing.md) - [Logging](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/observability-local/logging.md) - [DynoSim](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/dynosim.md): Simulate Dynamo deployment choices before spending GPU time - [Live Simulation with Mocker](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/dynosim/mocker.md) - [DynoSim Runs](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/dynosim/runs.md): Run one trace or synthetic workload through a simulated Dynamo configuration - [DynoSim Sweeps](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/dynosim/sweeps.md): Search simulated deployment candidates across topology, router, and SLA constraints - [Planner DynoSim Benchmarking](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/dynosim/planner-benchmarking.md): Drive the planner in the simulation loop against a saved trace to evaluate SLA behavior and scaling decisions - [Agents](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents.md): Agent-aware serving features in Dynamo - [Agent Tracing](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents/agent-tracing.md): Attach trajectory identity and export Dynamo request and tool-event telemetry - [Agent Hints](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents/agent-hints.md): Per-request serving hints for agentic workloads - [Priority Scheduling](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents/priority-scheduling.md): Request priority across the Dynamo router and backend engines - [Use Pi-Mono with Dynamo](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents/use-pi-mono-with-dynamo.md): Drive a real coding agent through Dynamo with agent context and tool tracing - [ThunderAgent Program Scheduler](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/agents/thunder-agent-program-scheduler.md): Program-level scheduling with tool-boundary pause/resume on top of KV-aware routing - [LoRA Adapters](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/lo-ra-adapters.md): Serve fine-tuned LoRA adapters with dynamic loading and routing in Dynamo - [Multimodal Model Serving](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/multimodal.md): Deploy multimodal models with image, video, and audio support in Dynamo - [Embedding Cache](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/multimodal/embedding-cache.md): Cache vision encoder embeddings to skip re-encoding repeated images - [Encoder Disaggregation](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/multimodal/encoder-disaggregation.md): Separate vision encoding into a dedicated worker for independent scaling - [Multimodal KV Routing](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/multimodal/multimodal-kv-routing.md): Route multimodal requests to workers with the best KV cache overlap - [Diffusion](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/diffusion.md): Deploy diffusion models for text-to-image, text-to-video, and more in Dynamo - [FastVideo](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/diffusion/fastvideo.md) - [Fastokens Tokenizer](https://docs.nvidia.com/dynamo/v1.2.1/user-guides/fastokens-tokenizer.md): Reduce frontend tokenization latency for long-context BPE models - [SGLang](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang.md) - [Reference Guide](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/reference-guide.md): Architecture, configuration, and operational details for the SGLang backend - [Examples](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/examples.md) - [Disaggregation](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/disaggregation.md) - [Diffusion](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/diffusion.md) - [SGLang Chat Processor](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/frontend-processor-fallback.md): SGLang-native preprocessing and postprocessing for chat completions - [Logits Processing](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/logits-processing.md) - [Observability](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/observability.md) - [SGLang for Agentic Workloads](https://docs.nvidia.com/dynamo/v1.2.1/backends/sg-lang/agentic-workloads.md): Priority scheduling and session control for multi-turn agentic serving - [TensorRT-LLM](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm.md) - [Reference Guide](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm/reference-guide.md): Features, configuration, and operational details for the TensorRT-LLM backend - [Examples](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm/examples.md) - [Prometheus](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm/observability.md) - [Video Diffusion Support (Experimental)](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm/diffusion-experimental.md) - [Known Issues and Mitigations](https://docs.nvidia.com/dynamo/v1.2.1/backends/tensor-rt-llm/known-issues-and-mitigations.md) - [vLLM](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm.md) - [Reference Guide](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/reference-guide.md): Configuration, arguments, and operational details for the vLLM backend - [vLLM Chat Processor](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/frontend-processor-fallback.md): vLLM-native preprocessing and postprocessing for chat completions - [Logits Processing](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/logits-processing.md) - [Examples](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/examples.md) - [KV Cache Offloading](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/kv-cache-offloading.md): CPU and disk offloading integrations for vLLM in Dynamo - [Prometheus](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/observability.md) - [vLLM-Omni](https://docs.nvidia.com/dynamo/v1.2.1/backends/v-llm/v-llm-omni.md) - [Custom Backend Overview](https://docs.nvidia.com/dynamo/v1.2.1/backends/custom-backend/custom-backend-overview.md): Choose the right path for bringing your own engine to Dynamo - [Writing Unified Backends](https://docs.nvidia.com/dynamo/v1.2.1/backends/custom-backend/writing-unified-backends.md): Choose Python or Rust for Dynamo's shared backend contract - [Writing Python Workers in Dynamo](https://docs.nvidia.com/dynamo/v1.2.1/backends/custom-backend/python-workers-lower-level.md): Create custom Python workers and engines for Dynamo - [Runtime Containers](https://docs.nvidia.com/dynamo/v1.2.1/backends/custom-backend/runtime-containers.md): Build Dynamo runtime images for built-in or custom backends - [Frontend](https://docs.nvidia.com/dynamo/v1.2.1/components/frontend.md) - [Frontend Guide](https://docs.nvidia.com/dynamo/v1.2.1/components/frontend/frontend-guide.md) - [Frontend Configuration Reference](https://docs.nvidia.com/dynamo/v1.2.1/components/frontend/configuration-reference.md): Complete reference for all frontend CLI arguments, environment variables, and HTTP endpoints - [Tokenizer](https://docs.nvidia.com/dynamo/v1.2.1/components/frontend/tokenizer.md) - [Router](https://docs.nvidia.com/dynamo/v1.2.1/components/router.md) - [Routing Concepts](https://docs.nvidia.com/dynamo/v1.2.1/components/router/routing-concepts.md): Cost model, worker selection, and routing primitives for the Dynamo router - [Configuration and Tuning](https://docs.nvidia.com/dynamo/v1.2.1/components/router/configuration-and-tuning.md): Router flags, event transport, load tracking, and tuning guidance - [Disaggregated Serving](https://docs.nvidia.com/dynamo/v1.2.1/components/router/disaggregated-serving.md): Prefill and decode routing with the Dynamo router - [Topology-Aware KV Transfer](https://docs.nvidia.com/dynamo/v1.2.1/components/router/topology-aware-kv-transfer.md): Runtime metadata and decode routing semantics for topology-aware prefill/decode handoff - [Router Operations](https://docs.nvidia.com/dynamo/v1.2.1/components/router/router-operations.md): Replica topology, state management, recovery, and remote indexers - [Router Examples](https://docs.nvidia.com/dynamo/v1.2.1/components/router/router-examples.md) - [Standalone KV Indexer](https://docs.nvidia.com/dynamo/v1.2.1/components/router/standalone-indexer.md): Run the KV cache indexer as an independent HTTP service for querying block state - [Standalone Slot Tracker](https://docs.nvidia.com/dynamo/v1.2.1/components/router/standalone-slot-tracker.md): Run active-request load accounting as an independent HTTP service - [KV Event Replay — Dynamo vs vLLM](https://docs.nvidia.com/dynamo/v1.2.1/components/router/kv-event-replay-dynamo-vs-v-llm.md): How the two systems handle gap detection, replay, and recovery for KV cache events - [Planner](https://docs.nvidia.com/dynamo/v1.2.1/components/planner.md) - [Planner Guide](https://docs.nvidia.com/dynamo/v1.2.1/components/planner/planner-guide.md) - [Global Planner Deployment Guide](https://docs.nvidia.com/dynamo/v1.2.1/components/planner/global-planner-guide.md) - [Planner Examples](https://docs.nvidia.com/dynamo/v1.2.1/components/planner/planner-examples.md) - [Profiler](https://docs.nvidia.com/dynamo/v1.2.1/components/profiler.md) - [Profiler Guide](https://docs.nvidia.com/dynamo/v1.2.1/components/profiler/profiler-guide.md) - [Profiler Examples](https://docs.nvidia.com/dynamo/v1.2.1/components/profiler/profiler-examples.md) - [KVBM](https://docs.nvidia.com/dynamo/v1.2.1/components/kvbm.md) - [Using HiCache](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kv-cache-integrations/hi-cache.md): Hierarchical KV caching with tier-aware router integration - [LMCache](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kv-cache-integrations/lm-cache.md) - [FlexKV](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kv-cache-integrations/flex-kv.md) - [KV Events for Custom Engines](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kv-cache-integrations/kv-events-for-custom-engines.md) - [LWS](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kubernetes-integrations/lws.md): LeaderWorkerSet integration for multinode Dynamo deployments - [Gateway API Inference Extension (GAIE)](https://docs.nvidia.com/dynamo/v1.2.1/integrations/kubernetes-integrations/gateway-api-inference-extension-gaie.md) - [Overall Architecture](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/overall-architecture.md): Architecture and components of the Dynamo inference runtime - [Architecture Flow](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/architecture-flow.md) - [Disaggregated Serving](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/disaggregated-serving.md) - [Distributed Runtime](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/distributed-runtime.md) - [Discovery Plane](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/communication-planes/discovery-plane.md) - [Request Plane](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/communication-planes/request-plane.md) - [Event Plane](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/communication-planes/event-plane.md) - [Router Design](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/component-design/router-design.md) - [KVBM Design](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/component-design/kvbm-design.md) - [Planner Design](https://docs.nvidia.com/dynamo/v1.2.1/design-docs/component-design/planner-design.md) - [How to Build and Publish Dynamo Docs](https://docs.nvidia.com/dynamo/v1.2.1/documentation/dynamo-docs-guide.md)